Benchmarks
Deep dives on every major LLM evaluation benchmark — what they measure, how to read scores, and which models lead.
MMLU: The Massive Multitask Language Understanding Benchmark
What MMLU measures, how it's constructed, why it became the standard LLM benchmark, what top model scores reveal, and when to use MMLU-Pro instead.
10 min read
HumanEval: OpenAI's Python Coding Benchmark Explained
How HumanEval measures LLM coding ability, what pass@k means, which models top the leaderboard, why it's now saturated, and what to use instead for real-world coding evaluation.
10 min read
GPQA: The Graduate-Level Benchmark That Still Challenges AI
What GPQA Diamond measures, how PhD-level questions are constructed to be Google-proof, why reasoning models dominate the leaderboard, and what scores above the human expert baseline really mean.
10 min readChatbot Arena: The Crowdsourced LLM Leaderboard Explained
How LMSYS Chatbot Arena's human-preference voting works, what the Elo system measures, why it captures what automated benchmarks miss, and how to read the rankings for model selection.
10 min readSWE-Bench: The Real-World Software Engineering Benchmark
How SWE-Bench tests AI on real GitHub issues, what SWE-Bench Verified measures, how agent systems approach the task, current leaderboard scores, and why it's the most predictive coding benchmark for engineering applications.
10 min readAIME: Why Competition Math Is the New Benchmark for AI Reasoning
What the American Invitational Mathematics Examination tests, why AI performance on AIME tracks genuine reasoning ability, current frontier scores, how reasoning models transformed the leaderboard, and what comes after AIME.
10 min readMT-Bench: Multi-Turn Conversation Evaluation
How MT-Bench evaluates model quality on multi-turn conversations using an LLM judge, what the 10-point scale measures, and how it complements other benchmarks.
6 min readTokens Per Second: Measuring LLM Generation Speed
What tokens per second (TPS) measures, how it affects real-world AI applications, which models are fastest, and how to interpret speed vs. quality tradeoffs.
6 min readTime to First Token (TTFT): The Most Important Latency Metric
Why time to first token defines perceived AI responsiveness, what drives TTFT differences between models and providers, and how to optimize for low-latency applications.
6 min readCost Per Million Tokens: The AI Economics Guide
How LLM API pricing works, why output tokens cost more than input, how to calculate actual task costs, and how prices have changed over time.
7 min readContext Length as a KPI: Why Window Size Matters
How context window size affects what AI models can do, the tradeoffs of longer contexts, and how to choose the right context length for your application.
7 min read